Integrating strong-motion recordings and twitter data for a rapid shakemap of macroseismic intensity
Bibliographic Data
| ID | 22028705 |
|---|---|
| Authors | Rosemary Fayjaloun (0000-0003-0402-8871, Bureau de Recherches Géologiques et Minières, corresponding author), Pierre Gehl (0000-0002-1225-9621, Bureau de Recherches Géologiques et Minières), Samuel Auclair (0000-0003-3490-668X, Bureau de Recherches Géologiques et Minières), Faïza Boulahya (Bureau de Recherches Géologiques et Minières), Simon Guérin-Marthe (0000-0003-3578-037X, Bureau de Recherches Géologiques et Minières), Agathe Roullé (0000-0002-5581-6643, Bureau de Recherches Géologiques et Minières) |
| Year | 2021 |
| Volume | 52 |
| Pages | 101927 |
| Publication date | 2021-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Disaster Risk Reduction (JOURNAL) |
| Journal identifiers | ISSN: 2212-4209 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijdrr.2020.101927 |
| OpenAlex | W3094888178 |
| Language | EN |
| Citations received | 1 |
| References cited | 27 |
Rapid estimation of the intensity of seismic ground motions is crucial for an effective rapid response when an earthquake occurs. To this end, maps of updated grond-motion fields (or shakemaps) are produced by using observations or measurements in near real-time to better constrain initial estimates. In this work, two types of observations are integrated to generate shakemaps right after an earthquake: the common type of data recorded by physical sensors (seismic stations) and the data extracted from social sensors (Twitter), or the combination of both. We investigate an approach to extract an approximation of the macroseismic intensity from social sensors 10 min after the earthquake; the approach relies on Twitter feeds to define the “felt area” where the earthquake was felt by the population, and the “unfelt locations” where the earthquake was not reported. Two recent earthquakes in France of moderate magnitude are studied and the results are compared to the official macroseismic intensity maps for validation. For the two studied cases, we note that Peak Ground Acceleration recordings far from the epicenter tend to underestimate the entire macroseismic field, and that the tweets from “felt areas” are complementary for a better estimation of the intensity shakemap. We highlight the importance and the limits of each type of observations when generating the seismic shakemaps
Acceleration · Epicenter · Estimation · Geodesy · Ground motion · Peak ground acceleration · Physics · Population · Seismology · Engineering · Landslides and related hazards · Seismic Waves and Analysis · Seismology and Earthquake Studies · Geology
Earthquake shakes Twitter users
St-Dbscan
Rapid assessment of disaster damage using social media activity
Where in the World Are You? Geolocation and Language Identification in Twitter
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
Citizens as sensors
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |